# Audit System and Batch Processing Guide
This guide covers Inferno's enterprise-grade audit logging system and comprehensive batch processing capabilities with cron scheduling, retry logic, and monitoring.
## Audit System Overview
The audit system provides comprehensive logging of all operations, security events, and system changes with encryption, compression, and multi-channel alerting capabilities.
### Key Features
- **Comprehensive Logging**: All operations, access attempts, and configuration changes
- **Encryption**: AES-256 encryption for sensitive audit data
- **Compression**: Efficient storage with configurable algorithms
- **Multi-channel Alerting**: Real-time notifications via email, Slack, webhooks
- **Compliance Reports**: Automated generation of audit reports
- **Tamper Detection**: Cryptographic integrity validation
- **Retention Policies**: Configurable data retention and archival
## Audit System Configuration
### Enable Audit Logging
```bash
# Enable audit system with encryption
inferno audit enable --encryption
# Enable with specific channels
inferno audit enable --channels email,slack,webhook
# Configure audit settings
inferno audit configure \
--encryption-key-file /secure/audit.key \
--compression gzip \
--retention-days 365 \
--max-size-gb 50
```
### Configuration File
```toml
# .inferno.toml
[audit]
enabled = true
log_level = "info" # trace, debug, info, warn, error
storage_path = "/var/log/inferno/audit"
[audit.encryption]
enabled = true
algorithm = "aes256"
key_file = "/secure/audit.key"
rotate_keys_days = 90
[audit.compression]
enabled = true
algorithm = "gzip" # gzip, zstd, lz4
level = 6
threshold_kb = 64
[audit.retention]
max_age_days = 365
max_size_gb = 100
archive_to_s3 = true
cleanup_interval_hours = 24
[audit.alerts]
enabled = true
channels = ["email", "slack", "webhook"]
severity_threshold = "warn"
[audit.alerts.email]
smtp_server = "smtp.company.com"
recipients = ["security@company.com"]
template = "security_alert"
[audit.alerts.slack]
webhook_url = "https://hooks.slack.com/services/..."
channel = "#security-alerts"
[audit.alerts.webhook]
url = "https://api.company.com/security/alerts"
headers = { "Authorization" = "Bearer token" }
```
## Audit Operations
### View Audit Logs
```bash
# View recent audit entries
inferno audit logs
# Filter by user and time
inferno audit logs --user admin --since 24h
# Filter by action type
inferno audit logs --action model_load,inference_request
# Export to file
inferno audit export --format json --output audit_report.json
# Search logs
inferno audit search --query "failed login" --since 7d
```
### Audit Log Management
```bash
# Check audit system status
inferno audit status
# Rotate audit logs
inferno audit rotate
# Verify log integrity
inferno audit verify --check-encryption
# Generate compliance report
inferno audit report --period monthly --format pdf
```
### Alert Configuration
```bash
# Test alert channels
inferno audit test-alerts
# Configure alert rules
inferno audit alert-rule create \
--name "multiple_failed_logins" \
--condition "failed_logins > 5 in 10m" \
--severity high \
--channels email,slack
# List alert rules
inferno audit alert-rules list
# Disable specific alerts
inferno audit alert-rule disable multiple_failed_logins
```
## Audit Event Types
### Authentication Events
```json
{
"timestamp": "2024-03-15T10:30:00Z",
"event_type": "authentication",
"action": "login_success",
"user": "admin",
"source_ip": "192.168.1.100",
"user_agent": "InfernoClient/1.0",
"session_id": "sess_123456",
"details": {
"auth_method": "api_key",
"permissions": ["admin:read", "admin:write"]
}
}
```
### Model Operations
```json
{
"timestamp": "2024-03-15T10:30:00Z",
"event_type": "model_operation",
"action": "model_load",
"user": "service_account",
"resource": "llama-7b-q4.gguf",
"details": {
"model_size_mb": 3800,
"backend_type": "gguf",
"load_time_ms": 2500,
"gpu_enabled": true
},
"result": "success"
}
```
### Security Events
```json
{
"timestamp": "2024-03-15T10:30:00Z",
"event_type": "security",
"action": "access_denied",
"user": "guest",
"source_ip": "192.168.1.200",
"resource": "/admin/config",
"details": {
"required_permission": "admin:read",
"user_permissions": ["inference:read"],
"reason": "insufficient_permissions"
},
"severity": "medium"
}
```
### System Events
```json
{
"timestamp": "2024-03-15T10:30:00Z",
"event_type": "system",
"action": "config_change",
"user": "admin",
"resource": "server_config",
"details": {
"changes": {
"max_concurrent_requests": {"old": 50, "new": 100},
"cache_enabled": {"old": false, "new": true}
}
},
"result": "success"
}
```
## Batch Processing System
The batch processing system provides production-ready job management with cron scheduling, retry logic, and comprehensive monitoring.
### Key Features
- **Cron Scheduling**: Full cron expression support for complex schedules
- **Job Queue**: Persistent job queue with priority handling
- **Retry Logic**: Configurable retry policies with exponential backoff
- **Resource Management**: CPU and memory limits per job
- **Monitoring**: Real-time job status and progress tracking
- **Dependencies**: Support for job chains and dependencies
## Batch Queue Configuration
### Enable Batch Processing
```bash
# Enable batch queue system
inferno batch-queue enable
# Configure queue settings
inferno batch-queue configure \
--max-concurrent 5 \
--retry-attempts 3 \
--default-timeout 3600
```
### Configuration File
```toml
[batch_queue]
enabled = true
storage_path = "/var/lib/inferno/queue"
max_concurrent_jobs = 5
default_timeout_seconds = 3600
[batch_queue.retry]
max_attempts = 3
base_delay_seconds = 30
max_delay_seconds = 3600
backoff_multiplier = 2.0
[batch_queue.resources]
default_cpu_limit = 2.0
default_memory_limit_gb = 4.0
monitor_interval_seconds = 30
[batch_queue.scheduling]
enable_cron = true
timezone = "UTC"
max_scheduled_jobs = 100
[batch_queue.notifications]
enabled = true
channels = ["email", "webhook"]
notify_on = ["job_failed", "job_completed"]
```
## Batch Job Operations
### Create and Manage Jobs
```bash
# Create a simple batch job
inferno batch-queue create \
--name "model_validation" \
--command "validate /models/*.gguf" \
--timeout 1800
# Create scheduled job with cron expression
inferno batch-queue create \
--name "nightly_conversion" \
--command "convert model /staging/model.pt /production/model.gguf" \
--schedule "0 2 * * *" \
--enabled
# Create job with dependencies
inferno batch-queue create \
--name "post_processing" \
--command "optimize /production/model.gguf" \
--depends-on "nightly_conversion"
# Create job with resource limits
inferno batch-queue create \
--name "heavy_processing" \
--command "benchmark --model large-model.gguf" \
--cpu-limit 4.0 \
--memory-limit 8GB \
--priority high
```
### Job Management
```bash
# List all jobs
inferno batch-queue list
# List running jobs
inferno batch-queue list --status running
# View job details
inferno batch-queue show job_123456
# Cancel a job
inferno batch-queue cancel job_123456
# Retry a failed job
inferno batch-queue retry job_123456
# Pause/resume queue
inferno batch-queue pause
inferno batch-queue resume
```
### Job Monitoring
```bash
# Monitor job progress
inferno batch-queue monitor job_123456
# View job logs
inferno batch-queue logs job_123456
# Get queue statistics
inferno batch-queue stats
# Export job history
inferno batch-queue export --format csv --output jobs.csv
```
## Cron Scheduling
### Cron Expression Format
```
┌───────────── minute (0 - 59)
│ ┌─────────── hour (0 - 23)
│ │ ┌───────── day of month (1 - 31)
│ │ │ ┌─────── month (1 - 12)
│ │ │ │ ┌───── day of week (0 - 6) (Sunday to Saturday)
│ │ │ │ │
* * * * *
```
### Common Cron Examples
```bash
# Every minute
inferno batch-queue create --schedule "* * * * *"
# Every hour at minute 0
inferno batch-queue create --schedule "0 * * * *"
# Daily at 2 AM
inferno batch-queue create --schedule "0 2 * * *"
# Weekly on Sunday at 3 AM
inferno batch-queue create --schedule "0 3 * * 0"
# Monthly on the 1st at midnight
inferno batch-queue create --schedule "0 0 1 * *"
# Every 15 minutes
inferno batch-queue create --schedule "*/15 * * * *"
# Weekdays at 9 AM
inferno batch-queue create --schedule "0 9 * * 1-5"
# Custom: Every 2 hours between 9 AM and 5 PM on weekdays
inferno batch-queue create --schedule "0 9-17/2 * * 1-5"
```
### Advanced Scheduling
```bash
# Multiple schedules for the same job
inferno batch-queue create \
--name "multi_schedule_job" \
--command "maintenance" \
--schedule "0 2 * * *" \
--additional-schedule "0 14 * * 6,0"
# Conditional scheduling
inferno batch-queue create \
--name "conditional_job" \
--command "cleanup if disk_usage > 80%" \
--schedule "0 */4 * * *" \
--condition "disk_usage_percent > 80"
```
## Job Types and Templates
### Model Processing Jobs
```bash
# Model conversion job
inferno batch-queue create \
--name "convert_models" \
--template model_conversion \
--input-dir /staging/models \
--output-dir /production/models \
--format gguf \
--schedule "0 1 * * *"
# Model validation job
inferno batch-queue create \
--name "validate_models" \
--template model_validation \
--model-dir /production/models \
--schedule "0 3 * * *"
```
### Inference Batch Jobs
```bash
# Batch inference job
inferno batch-queue create \
--name "batch_inference" \
--template batch_inference \
--model "llama-7b-q4" \
--input-file prompts.txt \
--output-file results.json \
--batch-size 10
```
### Maintenance Jobs
```bash
# Cache cleanup job
inferno batch-queue create \
--name "cache_cleanup" \
--template cache_maintenance \
--max-age 7d \
--max-size 10GB \
--schedule "0 4 * * *"
# Log rotation job
inferno batch-queue create \
--name "log_rotation" \
--template log_maintenance \
--max-files 10 \
--compress true \
--schedule "0 5 * * 0"
```
## Job Configuration Templates
### Model Conversion Template
```yaml
# templates/model_conversion.yaml
name: "model_conversion"
description: "Convert models between formats"
parameters:
- name: "input_dir"
type: "string"
required: true
- name: "output_dir"
type: "string"
required: true
- name: "format"
type: "enum"
values: ["gguf", "onnx", "pytorch"]
default: "gguf"
command: |
for model in ${input_dir}/*; do
inferno convert model "$model" "${output_dir}/$(basename "$model")" --format ${format}
done
resources:
cpu_limit: 2.0
memory_limit_gb: 4.0
timeout_seconds: 7200
retry:
max_attempts: 2
backoff_multiplier: 1.5
```
### Batch Inference Template
```yaml
# templates/batch_inference.yaml
name: "batch_inference"
description: "Process multiple inference requests"
parameters:
- name: "model"
type: "string"
required: true
- name: "input_file"
type: "string"
required: true
- name: "output_file"
type: "string"
required: true
- name: "batch_size"
type: "integer"
default: 10
command: |
inferno batch \
--model ${model} \
--input ${input_file} \
--output ${output_file} \
--batch-size ${batch_size}
resources:
cpu_limit: 4.0
memory_limit_gb: 8.0
gpu_required: true
timeout_seconds: 3600
```
## Monitoring and Alerting
### Job Monitoring
```bash
# Real-time job monitoring
inferno batch-queue monitor --real-time
# Performance dashboard
inferno batch-queue dashboard
# Resource usage monitoring
inferno batch-queue resources --job job_123456
```
### Alert Configuration
```bash
# Configure job alerts
inferno batch-queue alert-rule create \
--name "job_failure_rate" \
--condition "failure_rate > 0.10 in 1h" \
--action "notify_admin"
# Configure resource alerts
inferno batch-queue alert-rule create \
--name "high_resource_usage" \
--condition "cpu_usage > 90% for 5m" \
--action "scale_down"
```
## Integration Examples
### Python Batch Client
```python
from inferno_client import BatchQueue
from datetime import datetime, timedelta
# Initialize batch queue client
queue = BatchQueue("http://localhost:8080")
# Create a scheduled job
job = queue.create_job(
name="daily_model_sync",
command="rsync -av /staging/models/ /production/models/",
schedule="0 1 * * *",
timeout=3600,
retry_attempts=3
)
# Monitor job progress
def monitor_job(job_id):
while True:
status = queue.get_job_status(job_id)
print(f"Job {job_id}: {status['status']} - {status['progress']}%")
if status['status'] in ['completed', 'failed', 'cancelled']:
break
time.sleep(10)
# Schedule a one-time job
future_time = datetime.now() + timedelta(hours=2)
queue.schedule_job(
name="future_processing",
command="process_data --large-dataset",
run_at=future_time,
resources={
"cpu_limit": 8.0,
"memory_limit_gb": 16.0
}
)
```
### Kubernetes CronJob Integration
```yaml
apiVersion: batch/v1
kind: CronJob
metadata:
name: inferno-model-sync
spec:
schedule: "0 2 * * *"
jobTemplate:
spec:
template:
spec:
containers:
- name: model-sync
image: inferno:latest
command:
- inferno
- batch-queue
- create
- --name
- k8s-model-sync
- --command
- sync_models --source s3://models --dest /models
resources:
requests:
cpu: 1
memory: 2Gi
limits:
cpu: 2
memory: 4Gi
restartPolicy: OnFailure
```
## Best Practices
### Audit System
- Enable encryption for sensitive audit data
- Configure appropriate retention policies
- Set up multiple alert channels for redundancy
- Regularly verify audit log integrity
- Archive old logs to long-term storage
### Batch Processing
- Use appropriate resource limits for jobs
- Implement proper error handling and logging
- Test cron expressions before deployment
- Monitor job performance and adjust as needed
- Use job dependencies for complex workflows
### Security
- Encrypt audit logs containing sensitive data
- Use secure channels for alert notifications
- Implement proper access controls for job management
- Regularly rotate encryption keys
- Monitor for unusual job patterns or failures
### Performance
- Optimize job resource allocation
- Use compression for large audit logs
- Schedule resource-intensive jobs during off-peak hours
- Monitor queue performance and adjust concurrency limits
- Archive completed job data periodically